Urban MV and LV Distribution Grid Topology Estimation via Group Lasso

IEEE Trans. Control Netw. Syst.

Chertkov

Backhaus

2020

Optimal operation of distribution grid resources relies on accurate estimation of its state and topology. Practical estimation of such quantities is complicated by the limited presence of real-time meters. This paper discusses a theoretical framework to jointly estimate the operational topology and statistics of injections in radial distribution grids under limited availability of nodal voltage measurements. In particular we show that our proposed algorithms are able to provably learn the exact grid topology and injection statistics at all unobserved nodes as long as they are not adjacent. The algorithm design is based on novel ordered trends in voltage magnitude fluctuations at node groups, that are independently of interest for radial physical flow networks. The complexity of the designed algorithms is theoretically analyzed and their performance validated using both linearized and non-linear AC power flow samples in test distribution grids.

Section: A Prior Workmentioning

confidence: 99%

“…This assumption, similar to ones in [6], [7], [9], [12], is motivated by the fact that at short time-scales, injection fluctuations are the result of loads changes that are independent across nodes. Note that fluctuations at the same node may be aligned.…”

Section: Properties Of Voltage Second Momentsmentioning

confidence: 99%

Joint Estimation of Topology and Injection Statistics in Distribution Grids With Missing Nodes

IEEE Trans. Control Netw. Syst.

Chertkov

Backhaus

2020

“…For available line measurements, [4] uses a cycle basis and maximum likelihood tests to estimate the topology. When nodal voltage based measurements are available at all nodes, graphical model based formulations have been proposed to estimate the operational lines for both radial grids [5], [6] and loopy grids [7], [8]. In a similar measurement regime including nodal voltages, [9] present greedy topology learning schemes based on trends in second moments of voltage magnitudes.…”

Section: A Prior Workmentioning

confidence: 99%

Learning With End-Users in Distribution Grids: Topology and Parameter Estimation

Park

IEEE Trans. Control Netw. Syst.

Backhaus

et al. 2020

Efficient operation of distribution grids in the smartgrid era is hindered by the limited presence of real-time nodal and line meters. In particular, this prevents the easy estimation of grid topology and associated line parameters that are necessary for control and optimization efforts in the grid. This paper studies the problems of topology and parameter estimation in radial balanced distribution grids where measurements are restricted to only the leaf nodes and all intermediate nodes are unobserved/hidden. To this end, we propose two exact learning algorithms that use balanced voltage and injection measured only at the end-users. The first algorithm requires time-stamped voltage samples, statistics of nodal power injections and permissible line impedances to recover the true topology. The second and improved algorithm requires only time-stamped voltage and complex power samples to recover both the true topology and impedances without any additional input (e.g., number of grid nodes, statistics of injections at hidden nodes, permissible line impedances). We prove the correctness of both learning algorithms for grids where unobserved buses/nodes have a degree greater than three and discuss extensions to regimes where that assumption doesn't hold. Further, we present computational and, more importantly, the sample complexity of our proposed algorithm for joint topology and impedance estimation. We illustrate the performance of the designed algorithms through numerical experiments on the IEEE and custom power distribution models.

“…In a similar regime, [7], [8] presents greedy schemes based on trends in second moments of voltage magnitudes to identify grid topology. [9], [10] utilizes statistical independence tests, in the setting of a graphical model associated with nodal voltages, to reconstruct topology. A number of data driven and model-free schemes, using signature and regression based methods, were developed to reconstruct topology and line parameters [11]- [13].…”

Section: A Prior Workmentioning

confidence: 99%

Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability

Park

2018 Power Systems Computation Conference (PSCC)

Chcrtkov

2018

Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow control. This paper suggests a novel algorithm for learning the topology of distribution grid and estimating impedances of the operational lines with minimal observational requirements -it provably reconstructs topology and impedances using voltage and injection measured only at the terminal (enduser) nodes of the distribution grid. All other (intermediate) nodes in the network may be unobserved/hidden. Furthermore no additional input (e.g., number of grid nodes, historical information on injections at hidden nodes) is needed for the learning to succeed. Performance of the algorithm is illustrated in numerical experiments on the IEEE and custom power distribution models.